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Cited by 22,179Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsGene expression and cancer classification

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Open access
Li, Bo, Dewey, Colin N·2011·BMC Bioinformatics
DOI 10.1186/1471-2105-12-323PMC PMC3163565
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Abstract

Background: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results: We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions: RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Figures

Figure 1 The RSEM software workflow. The standard RSEM workflow (indicated by the solid arrows) consists of running just two programs (rsem-prepare-reference and rsem-calculate-expression), which automate the use of Bowtie for read alignment. Workflows with an alternative alignment program additionally use the steps connected by the dashed arrows. Two additional programs, rsem-bam2wig and rsem-plot-model, allow for visualizing the output of RSEM. RNA-Seq data can also be simulated with RSEM via the workflow indicated by the dotted arrows.
Figure 1 The RSEM software workflow. The standard RSEM workflow (indicated by the solid arrows) consists of running just two programs (rsem-prepare-reference and rsem-calculate-expression), which automate the use of Bowtie for read alignment. Workflows with an alternative alignment program additionally use the steps connected by the dashed arrows. Two additional programs, rsem-bam2wig and rsem-plot-model, allow for visualizing the output of RSEM. RNA-Seq data can also be simulated with RSEM via the workflow indicated by the dotted arrows.CC BY · PubMed Central
Figure 2 RSEM visualizations in the UCSC Genome Browser. Example visualizations of RSEM output from mouse RNA-Seq data set SRR065546 in the UCSC Genome Browser. (A) Simultaneous visualization of the wiggle output, which gives the expected read depth at each position in the genome, and the BAM output, which gives probabilistically-weighted read alignments. In the BAM track, paired reads are connected by a thin black line and the darkness of the read indicates the posterior probability of its alignment (black meaning high probability). (B) An example gene for which the expected read depth (top track) differs greatly from the read depth computed from uniquely-mapping reads only (bottom track).
Figure 2 RSEM visualizations in the UCSC Genome Browser. Example visualizations of RSEM output from mouse RNA-Seq data set SRR065546 in the UCSC Genome Browser. (A) Simultaneous visualization of the wiggle output, which gives the expected read depth at each position in the genome, and the BAM output, which gives probabilistically-weighted read alignments. In the BAM track, paired reads are connected by a thin black line and the darkness of the read indicates the posterior probability of its alignment (black meaning high probability). (B) An example gene for which the expected read depth (top track) differs greatly from the read depth computed from uniquely-mapping reads only (bottom track).CC BY · PubMed Central
Figure 3 Accuracy of four RNA-Seq quantification methods. The percent error distributions of estimates from RSEM, IsoEM, Cufflinks, and rQuant on simulated RNA-Seq data. The error distributions of global isoform and gene estimates from PE data are shown in (A) and (B), respectively. Global isoform and gene estimate error distributions for SE data are shown in (C) and (D), respectively.
Figure 3 Accuracy of four RNA-Seq quantification methods. The percent error distributions of estimates from RSEM, IsoEM, Cufflinks, and rQuant on simulated RNA-Seq data. The error distributions of global isoform and gene estimates from PE data are shown in (A) and (B), respectively. Global isoform and gene estimate error distributions for SE data are shown in (C) and (D), respectively.CC BY · PubMed Central
Figure 4 The directed graphical model used by RSEM. The model consists of N sets of random variables, one per sequenced RNA-Seq fragment. For fragment n, its parent transcript, length, start position, and orientation are represented by the latent variables Gn, Fn, Sn and On respectively. For PE data, the observed variables (shaded circles), are the read lengths ( and ), quality scores ( and ), and sequences ( and ). For SE data, , , and are unobserved. The primary parameters of the model are given by the vector θ, which represents the prior probabilities of a fragment being derived from each transcript.
Figure 4 The directed graphical model used by RSEM. The model consists of N sets of random variables, one per sequenced RNA-Seq fragment. For fragment n, its parent transcript, length, start position, and orientation are represented by the latent variables Gn, Fn, Sn and On respectively. For PE data, the observed variables (shaded circles), are the read lengths ( and ), quality scores ( and ), and sequences ( and ). For SE data, , , and are unobserved. The primary parameters of the model are given by the vector θ, which represents the prior probabilities of a fragment being derived from each transcript.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. Nicolae M, Mangul S, Măndoiu I, Zelikovsky A: Estimation of alternative splicing isoform frequencies from RNA-Seq data. In Algorithms in Bioinformatics, Lecture Notes in Computer Science. Edited by: Moulton V, Singh M. Liverpool, UK: Springer Berlin/Heidelberg; 2010:202–214. · DOI
  2. Taub M, Lipson D, Speed TP: Methods for allocating ambiguous short-reads. Communications in Information and Systems 2010, 10(2):69–82. · DOI

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Metadata

Authors
Li, B., & Dewey, C. N.
Affiliations
University of Wisconsin–Madison (US)
Corresponding author
Dewey, Colin N
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2011-08-04
Volume
12
Issue
1
Pages
323-323
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/1471-2105-12-323
PMC
PMC3163565
OpenAlex
W1999574084
Cited by
22,179
References
39
Citations by year
2026: 1650 · 2025: 2387 · 2024: 2647 · 2023: 2517 · 2022: 2448 · 2021: 2588 · 2020: 2146 · 2019: 1674 · 2018: 1311 · 2017: 1062 · 2016: 794 · 2015: 487 · 2014: 279 · 2013: 134 · 2012: 28
FWCI
57.58
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health
  • National Human Genome Research Institute (R01HG005232)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
RSEM, transcript quantification, RNA-seq, isoform abundance, gene expression, de novo transcriptome assembly, paired-end reads, single-end reads, credibility intervals
MeSH terms
Animals, Computer Simulation, Humans, RNA, Software, Sequence Analysis, RNA, Protein Isoforms, Gene Expression Profiling, Mice

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